Data science is one of the fastest-growing career paths today. According to the U.S. Bureau of Labour Statistics, the number of data scientist jobs will go up by 36% between 2023 and 2033.
Knowing the right programming languages is a big part of how far a student can go in this field.
The large number of libraries and the ease of learning make Python popular. SQL is still important for managing and getting information out of large datasets stored in databases. And languages like Julia are becoming more popular for high-performance computing tasks.
That’s why most BSc Data Science colleges in Pune focus on multiple programming languages rather than confining to one. Let’s check out the five most important languages every data analyst student must master.
What Makes a Programming Language Important in Data Science?
The programming language you specialise in determines your specific job role and projects you can work with in the future. So, make that decision wisely after considering the following factors:
- 1) Scalability – A language should be able to work with data tasks of all levels of difficulty, from looking at small datasets on a local machine to processing terabytes of data on cloud platforms. It lets you start small and grow your work for big data environments without having to switch to a whole new tech stack.
- 2) Library Support – Libraries are groups of code that have already been written so that you don't have to start from scratch. A data scientist can be more productive without having to come up with new ideas if the library ecosystem is rich.
- 3) Ease of Use – A language with clean and easy-to-read syntax comes with a shorter learning curve. It lets students focus more on the ideas behind data science instead of trying to figure out complicated code.
- 4) Job Market Demand – In the end, the reason you're learning these skills is to get a good job. The language you use should match what employers want. Almost eight in ten experts suggest Python for data science students, as it’s the most foundational language.
Languages VS Tools: What Students Need to Focus On
No doubts. Tableau, Power BI, and Excel are great for making graphs and getting quick information. But you need a strong hold over the programming knowledge to know how to use them effectively.
So first of all, you need to learn the languages to be a real data professional.
Relevance in Academics, Research, and Industry
When it comes to academics, Languages help students use algorithms and run simulations. And they can use it for statistical modelling in the research.
Similarly, in business, they help with everything from data pipelines to dashboards for real-time analytics. A well-chosen language works in all three areas without needing to learn a lot of new things.
Top Programming Languages for Data Science Students
Have a look at some of the most popular Programming languages:
1) Python
There is a reason why Python is the most popular language in data science: it is easy to learn and can handle complicated projects. It has a lot of libraries, such as:
- Pandas for cleaning data
- NumPy for doing math
- Matplotlib and Seaborn for making graphs
- Scikit-learn for machine learning.
2) R
R language is made just for statistical computing and analysis. That makes it a perfect fit for heavy academic research projects. Its ggplot2 library creates some of the most beautiful and insightful data visualisations you will ever see.
3) SQL
Most businesses keep their data in relational databases. SQL is the best way to get it out and handle it. You can filter millions of rows, join multiple tables, and group results in seconds with SQ.
4) Scala and Java
Java has been around for a long time, and because it is stable, it is a good choice for data systems in businesses. People often use it to make applications that process a lot of data. Scala, on the other hand, is shorter and combines functional and object-oriented programming.
5) Julia
Julia is a fairly new language, but it is growing quickly, especially in fields that need fast numerical computing. It is easy to write, like Python, but it runs almost as fast as C. This makes it great for scientific simulations and financial modelling.
Conclusion
A firm grasp of these programming languages will make you confident in handling data and prepare you for real industry challenges.
If you’re looking to build these skills with the right guidance, MIT College is one of the best BSc Data Science colleges in Pune. We offer you hands-on training and relevant projects to work with datasets to prepare you for the job market. Start your data science learning with us now!